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Digital twin enabled asset anomaly detection for building facility management

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Lu, Q 
Parlikad, AK 
Schooling, JM 

Abstract

Assets play a significant role in building utilities by undertaking the majority of their service functionalities. However, a comprehensive facility management solution that can help to monitor, detect, record and communicate asset anomalous issues is till nowhere to be found. The digital twin concept is gaining increasing popularity in architecture, engineering and construction/facility management (AEC/FM) sector, and a digital twin enabled asset condition monitoring and anomaly detection framework is proposed in this paper. A Bayesian change point detection methodology is tentatively embedded to reveal the suspicious asset anomalies in a real time manner. A demonstrator on cooling pumps is developed and implemented based on Centre for Digital Built Britain (CDBB) West Cambridge Digital Twin Pilot. The results demonstrate that supported by the data management capability provided by digital twin, the proposed framework realizes a continuous condition monitoring and anomaly detection for single asset, which contributes to efficient and automated asset monitoring in O&M management.

Description

Keywords

Building Information Modeling, Digital Twin, Facility Management, Asset Management, Condition Monitoring, Anomaly Detection

Journal Title

IFAC-PapersOnLine

Conference Name

4th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies

Journal ISSN

2405-8963
2405-8963

Volume Title

53

Publisher

Elsevier BV

Rights

All rights reserved
Sponsorship
Engineering and Physical Sciences Research Council (EP/N021614/1)
Technology Strategy Board (920035)
This research that contributed to this paper was supported by the Centre for Digital Built Britain (CDBB) with funding provided through the Government’s modern industrial strategy by Innovate UK, part of UK Research & Innovation. It was also partly funded by the EPSRC/Innovate UK Centre for Smart Infrastructure and Construction (Grant Numbers EP/N021614/1 and 920035).